Optunity is a library containing various optimizers for hyperparameter tuning.
Hyperparameter tuning is a recurrent problem in many machine learning tasks,
both supervised and unsupervised.This package provides several distinct approaches
to solve such problems including some helpful facilities such as cross-validation
and a plethora of score functions.

From an optimization point of view, the tuning problem can be considered as
follows: the objective function is non-convex, non-smooth and
typically expensive to evaluate. Tuning examples include optimizing regularization
or kernel parameters.

The figure below shows an example response surface, in which we optimized the
hyperparameters of an SVM with RBF kernel. This specific example is available at
Optimization response surface.

The Optunity library is implemented in Python and allows straightforward integration in other machine learning environments.
Optunity is currently also supported in R, MATLAB, GNU Octave and Java through Jython.

If you have any problems, comments or suggestions you can get in touch with us at gitter: